Modeling Neural Population Coordination via a Block Correlation Matrix

Presented a poster in the ISBA 2022

June 26, 2022


Present a poster in the 2022 World Meeting of the Society for Bayesian Analysis (ISBA 2022)


June 26 – July 1, 2022


12:00 AM


Montreal, Canada


Correlation matrix estimation is challenging. An unstructured correlation matrix is unestimable if p>n. Although extensive methods have been proposed, most of them only emphasize on computation efficiency but few of them provide clear interpretation. Motivated by a neuroscience study and financial market application, we consider a block structure on a correlation matrix to enjoy both interpretability and statistical efficiency. To circumvent intractable normalising constrants calculation resulting from block structure and valid correlation matrix constraints, we propose a novel model based on the canonical representation (Archakov and Hansen, 2020) in a Bayesian framework. I also incorporate a mixture of finite mixtures model (Miller and Harrison, 2018) to allow for estimating unknown block structure. We design a Gibbs sampling scheme where the parameters are updated by conjugacy.


Two rock stars (Jan and me) were discussing cool stuff seriously.

Posted on:
June 26, 2022
1 minute read, 136 words
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